django-celery-expert▌
vintasoftware/django-ai-plugins · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Expert guidance for Django Celery task design, configuration, error handling, and production monitoring.
- ›Covers task design patterns, Django ORM integration, transaction safety, and idempotency best practices
- ›Includes configuration for brokers, result backends, worker settings, queue routing, and task serialization
- ›Provides error handling strategies: retries with exponential backoff, dead letter queues, timeouts, and exception logging
- ›Supports periodic task scheduling with Celery
Django Celery Expert
Instructions
Step 1: Classify the Request
Identify the task category from the request:
- Django integration — transaction safety, ORM patterns, testing, request correlation → read
references/django-integration.md - Task design — new tasks, calling patterns, chains/groups/chords, idempotency → read
references/task-design-patterns.md - Configuration — broker setup, result backend, worker settings, queue routing → read
references/configuration-guide.md - Error handling — retries, backoff, dead letter queues, timeouts → read
references/error-handling.md - Periodic tasks — Celery Beat, crontab schedules, dynamic schedules, timezone handling → read
references/periodic-tasks.md - Monitoring — Flower, Prometheus, logging, debugging stuck tasks → read
references/monitoring-observability.md - Production deployment — scaling, supervision, containers, health checks → read
references/production-deployment.md
If the request spans multiple categories, read all relevant reference files before continuing.
Step 2: Read the Reference File(s)
Read each reference file identified in Step 1. Do not proceed to implementation without reading the relevant reference.
Step 3: Implement
Apply the patterns from the reference file. Before presenting the solution, verify:
- Task arguments are serializable (pass IDs, not model instances)
- Tasks with retries enabled are idempotent
- Errors are logged with context
- Long-running tasks have timeouts configured
Examples
Basic Background Task
Request: "Send welcome emails in the background after user registration"
# tasks.py
from celery import shared_task
from django.core.mail import send_mail
@shared_task(bind=True, max_retries=3)
def send_welcome_email(self, user_id):
from users.models import User
try:
user = User.objects.get(id=user_id)
send_mail(
subject="Welcome!",
message=f"Hi {user.name}, welcome to our platform!",
from_email="[email protected]",
recipient_list=[user.email],
)
except User.DoesNotExist:
pass
except Exception as exc:
raise self.retry(exc=exc, countdown=60 * (2 ** self.request.retries))
# views.py — queue only after the transaction commits
from django.db import transaction
def register(request):
user = User.objects.create(...)
transaction.on_commit(lambda: send_welcome_email.delay(user.id))
return redirect("dashboard")
Task with Progress Tracking
Request: "Process a large CSV import with progress updates"
@shared_task(bind=True)
def import_csv(self, file_path, total_rows):
from myapp.models import Record
with open(file_path) as f:
reader = csv.DictReader(f)
for i, row in enumerate(reader):
Record.objects.create(**row)
if i % 100 == 0:
self.update_state(
state="PROGRESS",
meta={"current": i, "total": total_rows},
)
return {"status": "complete", "processed": total_rows}
# Poll progress
result = import_csv.AsyncResult(task_id)
if result.state == "PROGRESS":
progress = result.info.get("current", 0) / result.info.get("total", 1)
Workflow with Chains
Request: "Process an order: validate inventory, charge payment, then send confirmation"
from celery import chain
@shared_task
def validate_inventory(order_id):
order = Order.objects.get(id=order_id)
if not order.items_in_stock():
raise ValueError("Items out of stock")
return order_id
@shared_task
def charge_payment(order_id):
order = Order.objects.get(id=order_id)
order.charge()
return order_id
@shared_task
def send_confirmation(order_id):
Order.objects.get(id=order_id).send_confirmation_email()
def process_order(order_id):
chain(
validate_inventory.s(order_id),
charge_payment.s(),
send_confirmation.s(),
).delay()
How to use django-celery-expert on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add django-celery-expert
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches django-celery-expert from GitHub repository vintasoftware/django-ai-plugins and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate django-celery-expert. Access the skill through slash commands (e.g., /django-celery-expert) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★49 reviews- ★★★★★Kofi Gonzalez· Dec 20, 2024
django-celery-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Meera Kapoor· Dec 16, 2024
Keeps context tight: django-celery-expert is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Chaitanya Patil· Dec 12, 2024
Useful defaults in django-celery-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Maya Wang· Dec 12, 2024
django-celery-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Henry Lopez· Dec 8, 2024
I recommend django-celery-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Henry Haddad· Nov 27, 2024
Solid pick for teams standardizing on skills: django-celery-expert is focused, and the summary matches what you get after install.
- ★★★★★Henry Rahman· Nov 11, 2024
django-celery-expert fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Layla Rao· Nov 7, 2024
We added django-celery-expert from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Piyush G· Nov 3, 2024
django-celery-expert has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Ishan Khanna· Nov 3, 2024
django-celery-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
showing 1-10 of 49